Handling Realistic Noise in Multi-Agent Systems with Self-Supervised Learning and Curiosity

JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH(2022)

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摘要
(1)Most reinforcement learning benchmarks - especially in multi-agent tasks - do not go beyond observations with simple noise; nonetheless, real scenarios induce more elaborate vision pipeline failures: false sightings, misclassifications or occlusion. In this work, we propose a lightweight, 2D environment for robot soccer and autonomous driving that can emulate the above discrepancies. Besides establishing a benchmark for accessible multi-agent reinforcement learning research, our work addresses the challenges the simulator imposes. For handling realistic noise, we use self-supervised learning to enhance scene reconstruction and extend curiosity-driven learning to model longer horizons. Our extensive experiments show that the proposed methods achieve state-of-the-art performance, compared against actor-critic methods, ICM, and PPO.
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关键词
deep reinforcement learning, multi-agent environment, autonomous driving, robot soccer, self-supervised learning
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